Optimizing View Change for Byzantine Fault Tolerance in Parallel Consensus

2026-01-14

AI Summary

This work addresses the performance degradation in parallel Byzantine Fault Tolerance (BFT) protocols caused by inefficient or unavailable node selection during view changes triggered by primary node failures. To tackle this challenge, the paper introduces, for the first time, a mixed-integer programming approach to optimize view change operations in parallel BFT, proposing the View Change Optimization (VCO) model. VCO jointly optimizes primary replica selection and follower reassignment by incorporating communication latency and fault scenarios. An efficient iterative algorithm for backup primary selection is developed using an enhanced decomposition method combined with Benders’ cutting-plane technique. Experimental evaluation on Microsoft Azure demonstrates that VCO-driven parallel BFT significantly outperforms existing approaches under both normal and faulty conditions, with performance gains amplifying as network scale increases.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The parallel Byzantine Fault Tolerant (BFT) protocol is viewed as a promising solution to address the consensus scalability issue of the permissioned blockchain. One of the main challenges in parallel BFT is the view change process that happens when the leader node fails, which can lead to performance bottlenecks. Existing parallel BFT protocols typically rely on passive view change mechanisms with blind leader rotation. Such approaches frequently select unavailable or slow nodes as leaders, resulting in degraded performance. To address these challenges, we propose a View Change Optimization (VCO) model based on mixed integer programming that optimizes leader selection and follower reassignment across parallel committees by considering communication delays and failure scenarios. We applied a decomposition method with efficient subproblems and improved benders cuts to solve the VCO model. Leveraging the results of improved decomposition solution method, we propose an efficient iterative backup leader selection algorithm as views proceed. By performing experiments in Microsoft Azure cloud environments, we demonstrate that the VCO-driven parallel BFT outperforms existing configuration methods under both normal operation and faulty condition. The results show that the VCO model is effective as network size increases, making it a suitable solution for high-performance parallel BFT systems.

Problem

• Byzantine Fault Tolerance
• view change
• leader selection
• parallel consensus
• performance bottleneck

Innovation

• View Change Optimization
• Parallel BFT
• Mixed Integer Programming
• Leader Selection
• Benders Decomposition